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Temporally Consistent Superpixels
AbstractSuperpixel algorithms represent a very useful and increasingly popular preprocessing step for a wide range of computer vision applications, as they offer the potential to boost efficiency and effectiveness. In this regards, this paper presents a highly competitive approach for temporally consistent superpixels for video content. The approach is based on energy-minimizing clustering utilizing a novel hybrid clustering strategy for a multi-dimensional feature space working in a global color subspace and local spatial subspaces. Moreover, a new contour evolution based strategy is introduced to ensure spatial coherency of the generated superpixels. For a thorough evaluation the proposed approach is compared to state of the art supervoxel algorithms using established benchmarks and shows a superior performance.
Related Material
[pdf][bibtex]@InProceedings{Reso_2013_ICCV,
author = {Reso, Matthias and Jachalsky, Jorn and Rosenhahn, Bodo and Ostermann, Jorn},
title = {Temporally Consistent Superpixels},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}
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